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Clustering based polyhedral conic functions algorithm in classification
1. | Department of Industrial Engineering, Faculty of Engineering, Anadolu University, Eskisehir, 26555, Turkey |
2. | Vitra, Eczacibasi Yapi Gerecleri, 11300 Bilecik, Turkey |
References:
[1] |
A. Astorino and M. Gaudioso, Polyhedral separability through successive LP,, Journal of Optimization Theory and Applications, 112 (2002), 265.
doi: 10.1023/A:1013649822153. |
[2] |
A. Astorino, M. Gaudioso and A. Seeger, Conic separation of finite sets. i: The homogeneous case,, Journal of Convex Analysis, 21 (2014), 001. Google Scholar |
[3] |
K. Bache and M. Lichman, UCI machine learning repository, 2013., URL , (). Google Scholar |
[4] |
A. M. Bagirov, Max-min separability,, Optimization Methods and Software, 20 (2005), 277.
doi: 10.1080/10556780512331318263. |
[5] |
A. M. Bagirov and J. Ugon, Supervised data classification via max-min separability,, Applied Optimization, 99 (2005), 175.
doi: 10.1007/0-387-26771-9_6. |
[6] |
A. M. Bagirov, M. Ghosh and D. Webb, A derivative-free method for linearly constrained nonsmooth optimization,, Journal of Industrial and Management Optimization, 2 (2006), 319. Google Scholar |
[7] |
A. M. Bagirov, J. Ugon, D. Webb, G. Ozturk and R. Kasimbeyli, A novel piecewise linear classifier based on polyhedral conic and max-min separabilities,, TOP, 21 (2013), 3.
doi: 10.1007/s11750-011-0241-5. |
[8] |
C. J. C. Burges, A tutorial on support vector machines for pattern recognition,, Data Mining and Knowledge Discovery, 2 (1998), 121. Google Scholar |
[9] |
R. N. Gasimov and G. Ozturk, Separation via polihedral conic functions,, Optimization Methods and Software, 21 (2006), 527.
doi: 10.1080/10556780600723252. |
[10] |
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The weka data mining software: An update,, SIGKDD Explorations, 11 (2009), 10.
doi: 10.1145/1656274.1656278. |
[11] |
R. Kasimbeyli, Radial epiderivatives and set-valued optimization,, Optimization, 58 (2009), 521.
doi: 10.1080/02331930902928310. |
[12] |
R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization,, SIAM J. on Optimization, 20 (2009), 1591.
doi: 10.1137/070694089. |
[13] |
R. Kasimbeyli and M. Mammadov, On weak subdifferentials, directional derivatives, and radial epiderivatives for nonconvex functions,, SIAM Journal on Optimization, 20 (2009), 841.
doi: 10.1137/080738106. |
[14] |
G. Ozturk, A New Mathematical Programming Approach to Solve Classification Problems,, PhD thesis, 6 (2007). Google Scholar |
[15] |
R. Rosenthal, GAMS: A User's Guide,, GAMS Development Corporation, (2013). Google Scholar |
[16] |
K. Schittkowski, Optimal parameter selection in support vector machines,, Journal of Industrial and Management Optimization, 1 (2005), 465.
doi: 10.3934/jimo.2005.1.465. |
show all references
References:
[1] |
A. Astorino and M. Gaudioso, Polyhedral separability through successive LP,, Journal of Optimization Theory and Applications, 112 (2002), 265.
doi: 10.1023/A:1013649822153. |
[2] |
A. Astorino, M. Gaudioso and A. Seeger, Conic separation of finite sets. i: The homogeneous case,, Journal of Convex Analysis, 21 (2014), 001. Google Scholar |
[3] |
K. Bache and M. Lichman, UCI machine learning repository, 2013., URL , (). Google Scholar |
[4] |
A. M. Bagirov, Max-min separability,, Optimization Methods and Software, 20 (2005), 277.
doi: 10.1080/10556780512331318263. |
[5] |
A. M. Bagirov and J. Ugon, Supervised data classification via max-min separability,, Applied Optimization, 99 (2005), 175.
doi: 10.1007/0-387-26771-9_6. |
[6] |
A. M. Bagirov, M. Ghosh and D. Webb, A derivative-free method for linearly constrained nonsmooth optimization,, Journal of Industrial and Management Optimization, 2 (2006), 319. Google Scholar |
[7] |
A. M. Bagirov, J. Ugon, D. Webb, G. Ozturk and R. Kasimbeyli, A novel piecewise linear classifier based on polyhedral conic and max-min separabilities,, TOP, 21 (2013), 3.
doi: 10.1007/s11750-011-0241-5. |
[8] |
C. J. C. Burges, A tutorial on support vector machines for pattern recognition,, Data Mining and Knowledge Discovery, 2 (1998), 121. Google Scholar |
[9] |
R. N. Gasimov and G. Ozturk, Separation via polihedral conic functions,, Optimization Methods and Software, 21 (2006), 527.
doi: 10.1080/10556780600723252. |
[10] |
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The weka data mining software: An update,, SIGKDD Explorations, 11 (2009), 10.
doi: 10.1145/1656274.1656278. |
[11] |
R. Kasimbeyli, Radial epiderivatives and set-valued optimization,, Optimization, 58 (2009), 521.
doi: 10.1080/02331930902928310. |
[12] |
R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization,, SIAM J. on Optimization, 20 (2009), 1591.
doi: 10.1137/070694089. |
[13] |
R. Kasimbeyli and M. Mammadov, On weak subdifferentials, directional derivatives, and radial epiderivatives for nonconvex functions,, SIAM Journal on Optimization, 20 (2009), 841.
doi: 10.1137/080738106. |
[14] |
G. Ozturk, A New Mathematical Programming Approach to Solve Classification Problems,, PhD thesis, 6 (2007). Google Scholar |
[15] |
R. Rosenthal, GAMS: A User's Guide,, GAMS Development Corporation, (2013). Google Scholar |
[16] |
K. Schittkowski, Optimal parameter selection in support vector machines,, Journal of Industrial and Management Optimization, 1 (2005), 465.
doi: 10.3934/jimo.2005.1.465. |
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